KF0: Kalman filter that treats also the aggregate variables as latent state variables.

KFnoX: Kalman filter that only treats the density coefficients as latent state variables. 
       The filter generates a likelihood for (Y,alpha-hat). 
       Once this is obtained, it is combined with p(X|alpha-hat)

PFnoX: Particle filter for linearized measurement equation. The likelihood function ignores p(X|alpha-hat),
       but it is later added just as for KFnoX.
       The output is compared to KFnoX to ensure that the particle filter is properly coded.

PFwithX: Particle filter for the nonlinear measurement equation. The likelihood value can be compared to KFnoX.

VAR0: VAR likelihood that completely ignores the ME in alpha-hat. 
      Resulting likelihood is combined with p(X|alpha-hat)

The results in the paper are based on running

- script_KFnoX.jl
- script_PFwithX.jl
- Graph_PFwithX_vs_KF_LogLH.m (Fig 1)

The outputs will be saved in the Results folder.

